In this article, we discussed data analytics, the types, enhancing production visibility and transparency, quality improvement and defect reduction, challenges and considerations in implementing data analytics.
Definition
Data analytics is the process of examining raw data using tools and techniques to find trends, answer questions, and derive meaningful, actionable insights, enabling better, data-driven decisions and predictions for businesses and organizations. It involves collecting, cleaning, transforming, and modeling data to uncover patterns, making sense of past and present information to guide future strategies, rather than relying on guesswork.
Features of Data Analytics:
Process: Collecting, cleaning, transforming, modeling, and analyzing data.
Goal: Discovering patterns, trends, and insights to support informed decision-making.
Techniques: Ranges from basic statistics to advanced machine learning and algorithms.
Tools: Includes spreadsheets (Excel), business intelligence software (Tableau, Power BI), data mining tools, and programming languages.
Applications: Optimizing business performance, understanding customer behavior, predicting future trends, and solving specific challenges.
Types of Analytics Used in Production
Data analytics in production can be categorized into four main types:
Descriptive analytics – explains what has happened
Diagnostic analytics – explains why it happened
Predictive analytics – forecasts what is likely to happen
Prescriptive analytics – recommends what actions to take.
Enhancing Production Visibility and Transparency:
Technology Adoption: Implement IoT sensors for real-time tracking, AI for predictive analytics, and Blockchain for immutable, verifiable data.
Supplier Collaboration: Establish clear expectations, data-sharing protocols, and shared platforms with suppliers for collective problem-solving.
End-to-End Mapping: Visualize the entire supply chain to identify bottlenecks, risks, and areas for improvement.
Data-Driven Decisions: Use analytics to proactively manage disruptions and make informed choices.
Ethical & Sustainable Sourcing: Integrate transparency into sourcing to build consumer trust and meet compliance.
Clear Communication: Maintain open channels with suppliers and internal teams, overcommunicating where necessary.
Quality Improvement and Defect Reduction:
Six Sigma (DMAIC): A data-driven approach (Define, Measure, Analyze, Improve, Control) to reduce process variability and defects, aiming for near perfection (3.4 defects per million opportunities).
Quality Management Systems (QMS): Implementing comprehensive systems for planning, assurance, and control to monitor quality throughout the production cycle.
Root Cause Analysis: Using tools like cause-and-effect diagrams to find why defects happen (e.g., machine, man, method, materials) and prevent recurrence.
Total Quality Management (TQM): A holistic approach involving all employees and departments in continuous quality improvement.
Define & Measure: Clearly define quality goals and collect baseline data on current defect rates (e.g., using statistical process control).
Analyze: Use data to pinpoint the sources and patterns of defects, looking at variations in machines, processes, and human factors.
Improve: Implement solutions like process automation, better training, or updated tools to eliminate identified root causes.
Control: Establish monitoring systems (like real-time monitoring and KPIs) to sustain improvements and prevent defects from creeping back in.
Challenges and Considerations in Implementing Data Analytics:
Data Quality & Integration: Inaccurate, incomplete, or inconsistent data (“garbage in, garbage out”) and data scattered across disparate systems hinder reliable analysis.
Data Volume & Variety: Managing massive datasets (Big Data) in structured, semi-structured, and unstructured formats is complex.
Skills Gap: Shortage of analysts with expertise in SQL, Python, Machine Learning, and advanced analytics.
Technology & Infrastructure: Outdated systems or inability to scale analytics platforms.
Data Silos: Data trapped in different departments makes consolidation difficult.
Security & Privacy: Protecting sensitive data and ensuring regulatory compliance (like GDPR).
Resistance to Change: Employees hesitant to adopt data-driven decision-making.
Lack of Strategy: No clear goals or understanding of how analytics drives business value.
Conclusion
Data analytics significantly improves production decision-making by transforming raw data into actionable insights that enhance efficiency, quality, reliability, and strategic planning. Through real-time monitoring, predictive analysis, and scenario modeling, organizations can make faster, more accurate, and more confident production decisions.
READ: Data Analytics in Driving Digital Transformation